• Design, develop, and deploy AI solutions leveraging LLMs, RAG, and agentic AI architectures • Design, evaluate, and maintain a seamless generative pipeline that accepts user queries/prompts in conversational language, handles real-time prompt orchestration to generate safe SQL, extracts required insights, and reformats raw datasets into clean, actionable, human-readable answers • Evaluate business processes and identify opportunities where generative AI and autonomous agents can improve efficiency, decision-making, and customer outcomes • Architect multi-agent and agentic workflows using modern frameworks • Develop AI systems that effectively utilize tools, APIs, enterprise data sources, and external services to perform complex tasks • Implement prompt engineering, model evaluation, guardrails, observability, and governance practices for production AI applications • Build and optimize RAG pipelines, vector databases, knowledge retrieval systems, and semantic search capabilities • Assess emerging AI models and technologies to determine the most effective and cost-efficient solutions for client needs • Integrate AI capabilities into enterprise software ecosystems, cloud platforms, and business workflows • Design and implement AI monitoring, testing, evaluation, and continuous improvement processes • Collaborate with business stakeholders, solution architects, software engineers, and data scientists to deliver AI-driven solutions • Present AI concepts, recommendations, architectures, and implementation strategies to technical and executive audiences

Role Description Pinnipedia is looking for a freelance AI Engineer to turn messy inputs into structured knowledge and reliable answers. Your Mission: - Own the end-to-end pipeline that turns unstructured documents into a validated, queryable knowledge graph. - Accountable for extraction quality, graph integrity, and the data layer that backs the product's read path. Tasks: - LLM extraction pipelines: - Document chunking, property and relationship extraction, cross-chunk reconciliation, gap detection. - Built with structured-output LLM agents orchestrated by durable workflows. - Knowledge graph: - Schema design as typed Pydantic models, Cypher access patterns and indexing strategy, graph operations, schema evolution and migration. - Scope ends at the graph boundary: API contracts and query abstractions exposed to consumers belong to the full-stack engineer. - Deterministic rule engines: - Table-driven evaluators for cases where code beats LLM judgment; clear contracts between deterministic and probabilistic components. - Data validation & quality: - Schema enforcement, required-property contracts, audit trails, eval harnesses (expert review, unsupervised checks, synthetic fixtures, LLM-as-judge). - Live data ops: - Backfills, coordinated migrations across relational + graph stores, observability on extraction throughput and quality, incident response. Qualifications - 5+ years shipping data/AI systems to production with real customers. - Strong Python (typed, modern) and SQL. Comfortable with PostgreSQL under load. - Production experience with at least one graph database (Neo4j preferred; Neptune, ArangoDB, TigerGraph acceptable). - Production LLM pipeline experience: structured output, agent orchestration, prompt and version management, evaluation frameworks. - Durable workflow orchestration in production (DBOS, Temporal, Airflow, Prefect, Dagster). - Test-first discipline - integration tests against real datastores (Testcontainers or equivalent). - Fluent English skills. - Freelance status. Requirements - Experience with regulated, compliance-driven, or standards-heavy extraction domains (legal, medical, financial, security/audit). - Designed deterministic evaluators alongside LLM components and knows when to reach for which. - Contributions to data contracts, schema governance, or ontology work. - German language skills. Benefits - Remote, full-time with flexible scheduling on a freelance basis. - CET (Berlin) timezone availability expected. - Possibility of relocation if successful work relationship is achieved after a period of time. - Competitive salary: 32.000–42.000 € base (premium for exceptional senior profiles). - Small, focused team; direct collaboration with the Product Owner and Full-Stack Engineer. - Modern tooling, real ownership, and a learning budget for role-relevant training. - Impact: help SMEs meet rising security requirements with less friction. Company Description Pinnipedia Technologies GmbH is a Berlin startup building a cloud platform that automates and assists the creation of audit-ready IT-security concepts for German/EU standards (e.g., BSI-Grundschutz, C5). Our product combines LLMs, a domain knowledge graph, and workflow automation to turn scattered inputs (policies, asset lists, audits) into draft security concepts, checklists, risk/impact matrices, and evidence that stands up in audits. We are IGP-funded (2025/26) and co-develop with FU Berlin and pilot users from industry and security consulting. By Aug 2026 we aim to ship a multi-tenant SaaS with hardened cloud setup, CI/CD, quality dashboards and a go-to-market kit. We work as a small, hands-on team with a modern stack, pragmatic documentation, and a focus on reliable, compliant outcomes for SMEs.

• Design, develop, and ship AI-driven capabilities—including agents, intelligent assistants, context-aware prediction, and generative AI—using Python, top-tier LLMs, TypeScript, React, Canvas, and WebGL • Engineer high-performing AI workflows leveraging LLMs (OpenAI, Anthropic Claude, Google Gemini), prompt engineering, and generative techniques—ensuring reliability, scalability, and maintainability • Execute end-to-end AI projects from conceptualization and prototyping to production code, collaborating closely with product managers, designers, and engineers • Experiment and iterate on AI capabilities within your team domain, staying ahead of emerging AI tools, frameworks, and techniques • Champion best practices in AI system development—including evaluations, LLMOps, CI/CD, observability, and ethical AI principles • Measure and optimize AI performance, establishing benchmarks for user engagement, model accuracy, latency, error rate, and drift • Write clean, well-tested, maintainable code, participating in code reviews and technical discussions across the stream • Troubleshoot and resolve production issues, ensuring high availability and performance

Role Description As an AI Engineer at Netguru, you will be: - Implementing Azure OpenAI API for applications. - Implementing RAG systems. - Working with open source models to fine-tune and apply them for clients. - Experimenting with different ML techniques. - Creating PoC, MVP of AI applications. - Designing, developing, and deploying machine learning models. - Leading AI project implementations. - Collaborating with cross-disciplinary teams. - Researching new techniques, models and approaches to apply AI in retail, healthcare, finance and general industries. Qualifications - Previous experience with software development (ideally 3-4 years of commercial experience). - Strong proficiency in Python and relevant AI frameworks. - ~2 years of hands-on experience with LLM/GenAI applications. - Knowledge how RAG, embeddings, vector databases work. - Experience with agentic workflows. - Familiarity with OpenAI / Microsoft APIs. - Experience with cloud platforms (AWS or Microsoft). - Experience with Langchain, autogen (AI Agent) or other LLM agentic framework. - Excellent communication skills in English – both verbal and written (C1+). - Based in European Union. Requirements - Experience with open-source LLMs (e.g. Llama, Mistral). - Experience with AI/ML frameworks such as PyTorch or TensorFlow. - Experience with voice AI or multimodal systems. - AI-related certifications. - Frontend and backend development experience. Benefits - Access to the WorkSmile platform offering benefits adapted to your preferences: - Multisport card. - Private health insurance package. - Life insurance. - And hundreds of other options to choose from 15 categories (shopping, leisure, travel, food, etc.). - Discounts on Apple products. - Performance Bonus. - Various internal initiatives: webinars, knowledge-sharing sessions, internal conferences. - 16 paid days off (B2B).